Stock and market index prediction using Informer network

ArXiv ID: 2305.14382 “View on arXiv”

Authors: Unknown

Abstract

Applications of deep learning in financial market prediction has attracted huge attention from investors and researchers. In particular, intra-day prediction at the minute scale, the dramatically fluctuating volume and stock prices within short time periods have posed a great challenge for the convergence of networks result. Informer is a more novel network, improved on Transformer with smaller computational complexity, longer prediction length and global time stamp features. We have designed three experiments to compare Informer with the commonly used networks LSTM, Transformer and BERT on 1-minute and 5-minute frequencies for four different stocks/ market indices. The prediction results are measured by three evaluation criteria: MAE, RMSE and MAPE. Informer has obtained best performance among all the networks on every dataset. Network without the global time stamp mechanism has significantly lower prediction effect compared to the complete Informer; it is evident that this mechanism grants the time series to the characteristics and substantially improves the prediction accuracy of the networks. Finally, transfer learning capability experiment is conducted, Informer also achieves a good performance. Informer has good robustness and improved performance in market prediction, which can be exactly adapted to real trading.

Keywords: time-series forecasting, Informer network, Transformer, intra-day prediction, Equities

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced deep learning architectures (Informer, ProbSparse self-attention) with significant mathematical novelty, yet it also conducts a structured empirical comparison using standard financial metrics (MAE, RMSE, MAPE) on real intraday stock/index data, making it both theoretically dense and implementation-focused.

  flowchart TD
    A["Research Goal<br>Intra-day Stock Prediction"] --> B["Data Preparation<br>1 & 5-min Frequencies"]
    B --> C{"Model Comparison"}
    C --> D["Informer<br>with Time Stamps"]
    C --> E["LSTM"]
    C --> F["Transformer"]
    C --> G["BERT"]
    
    D --> H["Performance Evaluation<br>MAE, RMSE, MAPE"]
    E --> H
    F --> H
    G --> H
    
    H --> I["Key Findings<br>Informer: Best Performance<br>Time Stamps: Critical<br>Transfer Learning: Effective"]